Projecting future air temperature of Hong Kong for the 21st century and its implications on urban planning and design.

• Other Authors: Lau, Ka Lun (author.)
• Format: Others
• Language: English; Chinese
• Published: 2013
• Subjects: City planning > Environmental aspects; City planning > Environmental aspects > China > Hong Kong; Climatic changes > forecasting; Climatic changes > China > Hong Kong > forecasting; Urban temperature; Urban temperature > China > Hong Kong
• Online Access: http://library.cuhk.edu.hk/record=b6116181; http://repository.lib.cuhk.edu.hk/en/item/cuhk-1077704

近幾十年來，全球氣候變化──特別是城市氣候變化──影響城市環境及居民生活質素的程度已引起公眾廣泛的討論。然而，過去研究一般採用之低空間解析度並不足夠為城市規劃及設計提供完善的資訊，引致對於氣候變化缺乏充分的考慮。高密度的城市環境（如香港）需要高時間解析度的氣候數據以制定有效的適應和減緩策略來應對未來氣候的變化。 === 本研究採用線性迴歸技術，以預測未來香港市區和郊區的氣溫。本研究利用氣象站和統計延伸得出之基線氣溫數據來建立統計降尺度模型，以預測未來香港市區和郊區之平均氣溫、最高氣溫和最低氣溫。 === 根據結果顯示，統計降尺度模型能夠有效建立大氣氣象參數和本港氣溫兩者之間的關係，尤其春季、秋季和冬季之氣溫預測表現理想。另外，冬季氣溫的上升趨勢則出現較大的升幅。研究結果亦顯示夜間氣溫的上升趨勢一般比日間為高。在未來的日子，郊區的溫度上升亦將會比市區為高。隨著城市化的影響納入預測溫度因素之中，預計郊區的氣溫將超過城市核心（天文台總部之氣象站），而郊區暖化的速度亦比市區和近郊為高。 === 本研究發現統計降尺度方法能有助利用全球氣候模型（GCM）提供之數據，以預測未來氣候之變化。城市規劃與設計過程是需要大量的數據進行評估氣候變化對城市環境的影響之研究，儘管統計降尺度方法有一定程度的局限性，它仍然是一個低成本而有效的方法。根據未來預測之氣溫，本研究具體指出未來之氣候變化對於城市規劃和設計過程的影響，亦提出了一系列於不同規劃層面適用之適應和減緩措施的建議。 === The effects of global climate change on urban environment have been widely discussed in recent decades. In particular, changes in urban climate have received much attention as they affect the living quality of urban dwellers. However, the coarse spatial scales employed in recent climate change studies were found to be insufficient in the context of urban planning and design. It leads to the lack of information on the changing urban climate and insufficient consideration of climate change in urban planning and design processes. In high-density cities like Hong Kong, the complex urban environment requires climatic data at very fine temporal resolution in order to formulate effective adaptation and mitigation strategies for future climate change. === The present study employed regression techniques to establish empirical relationship between large-scale predictor variables and local predictands in order to obtain future air temperature of urban and rural areas of Hong Kong. 40-year baseline conditions of local air temperature were obtained from both the observational and statistically extended temperature record. Monthly means of daily mean, maximum, and minimum air temperatures for both daytime and night-time were calculated for establishing statistical downscaling (SD) models to project future air temperature of urban and rural areas of Hong Kong. === The results suggest that regression-based downscaling techniques are able to capture the relationship between large-scale atmospheric conditions and station-scale meteorological parameters. The SD models performed particularly well in winter and considerably satisfactory results were obtained in spring and autumn. Night-time temperature trends generally exhibited greater increases than daytime trends. Seasonal variations were present with greatest increases observed in winter. Rural areas would likely experience greater warming than the urban areas in the future. With urbanization effect incorporated into the projected temperature series, it was found that air temperature projected for suburban stations would exceed that for the urban core. Rural warming also exhibited a higher rate than those observed in suburban and urban stations. === The present study shows that statistical downscaling approach provides a method to obtain information about future climatic conditions at local scale by using GCM outputs which are widely accepted to be useful tools to assist climate change studies. Despite of the limitations that historical climate would persist in projected climatic series, it allows a low-cost but effective measure for climate impact assessments, particularly in the context of urban planning and design, which requires extensive data for a wide range of studies. Based on the projected air temperature, implications of future climate change on urban planning and design of potential development were discussed and recommendations on potential adaptation and mitigation measures at different planning levels were also presented. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Lau, Ka Lun. === Thesis (Ph.D.) Chinese University of Hong Kong, 2013. === Includes bibliographical references (leaves 159-173). === Abstracts also in Chinese.